Application of Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Gregory Bower Curtis Wrable Ross Bird Paul Woodford

Abstract

Synthetic aperture radars are radar platforms that generate detailed images through radio frequency transmission and receiving. These systems can be high peak power, complex systems that can suffer from internal component or subsystem degradation. In addition, the operational environment can also affect the final image of the radar due to scene-based radio frequency interference (RFI). Because of these effects, it is ideal to be able to identify, classify, and quantify the degradation of these systems in order to optimize their performance and life. The work presented in this paper is an extension of QorTek’s previous work using Symbolic Analysis to detect degradation using the radar’s phase history data. In conjunction with the KEYW, Corp., QorTek has acquired field data to train and test its algorithm. To test the trained algorithm, a prototype hardware/software system integrating the SA approach was designed, built and flown on a test flight piggybacking on a radar system provided by KEYW. The initial results were very positive and also identified areas of improvement. The training and test results as well as the flight-test plan and results are presented in this paper. The paper concludes with specific improvements to be made to the algorithm for the next round of radar integration and flight-testing.

How to Cite

Bower, G., Wrable, . C. ., Bird , R. ., & Woodford, P. . (2015). Application of Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2685
Abstract 2 | PDF Downloads 2

##plugins.themes.bootstrap3.article.details##

Keywords

diagnostics, Synthetic Aperture Radar, Radar Degradation

References
Bower, G., Mayer, J., & Reichard, K. (2011). "Symbolic Dynamics and Analysis of Time Series Data for Diagnostics of a dc-dc Forward Converter," in Annual Conference of the Prognostics and Health Management Society, Montreal, 2011.

Bower, G., Zook, J., Bird, R. (2014). “Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads” in Annual Conference of the Prognostics and Health Management Society, Fort Worth, 2014.

Daw, C.S., C.E.A. Finney & E.R. Tracy (2003). "A review of symbolic analysis of experimental data." Review of Scientific Instruments 74.2 (2003): 915-930.

Kwok L. Tsui, Nan Chen, Qiang Zhou, Yizhen Hai, and Wenbin Wang (2014), “Prognostics and Health Management: A Review on Data Driven Approaches,” Mathematical Problems in Engineering, Article ID 793161, in press.

Lord, R. T. (2005) “Radio Frequency Interference Suppression applied to Synthetic Aperture Radar Data”, XXVIIIth General Assembly of International Union of Radio Science, URSI 2005, New Delhi, India.

Meyer, F.J.; Nicoll, J.B.; Doulgeris, A.P., (2013) "Correction and Characterization of Radio Frequency Interference Signatures in L-Band Synthetic Aperture Radar Data,"
IEEE Transactions on Geoscience and Remote Sensing,vol.51, no.10, pp.4961-4972.

Ray, Asok (2004). "Symbolic dynamic analysis of complex systems for anomaly detection." Signal Processing (2004): 1115-1130.

Schwabacher, M. 2005. “A Survey of Data-Driven Prognostics.” Proceedings of the AIAA Infotech@Aerospace Conference. Reston, VA: American Institute for Aeronautics and Astronautics, Inc.

Sarkar S, Jin X, Ray A. (2011) “Data-Driven Fault Detection in Aircraft Engines with Noisy Sensor Measurements.” ASME. J. Eng. Gas Turbines Power.
Section
Technical Papers